UNIVERSITI PUTRA MALAYSIA

MOBILE ROBOT LOCALIZATION USING BAR CODES AS ARTIFICIAL LANDMARKS

MAHMUD M. M. BEN-HA

FK 2000 28

MOBILE ROBOT LOCALIZATION USING BAR CODES AS ARTIFICIAL LANDMARKS

By

MAHMUD M. M. BEN-HA

Thesis Submitted in FulnIment of the Requirements for the Degree of Master of Science in the Faculty of Engineering

Universiti Putra Malaysia

May 2000

ii

Dedicated to My parents, my wife, my son, and the rest of my family

iii

Abstract of thesis presented to the Senate ofUniversiti Putra Malaysia in fulfilment of the requirements for the degree of Master of Science.

MOBILE ROBOT LOCALIZATION USING BAR CODES AS ARTIFICIAL LANDMARKS

By

MAHMUD M. M. BEN-HAMID

May 2000

Chairman: Md. Mahmud Hasan, Ph.D.

Faculty: Engineering

"Where am f' is the central question in mobile robot navigation. Robust and

reliable localization are of vital importance for an autonomous mobile robot because

the ability to constantly monitor its position in an unpredictable, unstructured, and

dynamic environment is the essential prerequisite to build up and/or maintain

environmental maps consistently and to perform ·path planning. Thus, self-

localization as precondition for goal-oriented behavior is a fundamental property an

autonomous mobile robot needs to be equipped with.

Accurate, flexible and low-cost localization are important issues for achieving

autonomous and cooperative motions of mobile robots. Mobile robots usually

perform self-localization by combining position estimates obtained from odometry or

inertial navigation with external sensor data.

The objective of the thesis is to present a pragmatic idea which utilizes a

camera-based bar code recognition technique in order to support mobile robot

localization In indoor environments. The idea is to further improve already existing

localization capabilities, obtained from dead-reckoning, by furnishing relevant

iv

environmental spots such as doors, stairs, etc. with semantic information. In order to

facilitate the detection of these landmarks the employment of bar codes is proposed.

The important contribution of the thesis is the designing of two software

programs. The first program is the bar code generation program which is able to

generate five types of bar code labels that play a major role in the proposed

localization method. The second program is the bar code recognition program that

analyzes image files looking for a bar code label. If a label is found the program

recognizes it and display both the information it contains and its coding type.

Results concerning the generation of five types of bar code labels which are

code 2 of 5, code 3 of9 , codabar code, code 128 and code 2 of 5 interleaved and the

detection and identification of these labels from image fi)es are obtained.

In conclusion the thesis proposes a solution to mobile robot self-localization

problem, which is the central significant for implementing an autonomous mobile

robot, by utilizing a camera-based bar code recognition technique to support the

basic localization capabilities obtained from a dead-reckoning method in an indoor

environment.

Abstrak tesis yang dikemukakan kepada Senat Universiti Putra Malaysia Sebagai memenuhi keperluan untuk ijazah Master Sains

LOKALISASI ROBOT BERGERAK MENGGUNAKAN KOD BAR SEBAGAI MERCUTANDA TIRUAN

Oleh

MAHMUD M. M. BEN-HAMID

Mei 2000

Pengerusi: Md. Mahmud Hasan, Ph.D.

Fakulti: Kejuruteraan

v

"Di manakah saya" adalah soalan penting dalam pengemudian robot bergerak.

Ketegapan dan lokalisasi yang boleh harap adalah perkara penting bagi sesebuah

robot bergerak berautonomi kerana kebolehannya memantau secara seragam

posisinya dalam suasana-suasana yang pelbagai seperti suasana yang tidak boleh

diramalkan, tidak terstruktur dan dinamik adalah prakeperluan yang penting untuk

membina danlatau mengekalkan peta-peta alam sekitar secara konsisten dan

terancang. Maka lokalisasi diri selaku prasyarat bagi sifat yang mementingkan

matlamat adalah ciri asas suatu robot bergerak berautonomi.

Ciri-ciri ketepatan, kefleksibelan dan harga yang rendah bagi sesuatu lokalisasi

adalah perkara penting bagi mencapai gerakan-gerakan robot bergerak yang

berautonomi dan koperatif Robot bergerak biasanya melaksanakan lokalisasi diri

dengan menggabungkan nilai-nilai posisi yang diperolehi daripada odometri atau

pengemudian inersia oleh data penderia luaran.

Matlamat tesis ini adalah untuk mengemukakan idea pragmatik yang

memanfaatkan teknik pengenalan kod bar berdasarkan-kamera bagi menyokong

vi

pemusatan setempat robot bergerak dalam persekitaran dalaman. Idea ini adalah untuk

mempertingkatkan lagi keupayaan pemusatan setempat tersedia ada, diperolehi dari

kaedah perhitungan-mutlak, dengan memperlengkapkan sasaran persekitaran yang

relevan seperti pintu, tangga dsb., dengan maklumat semantik. Bagi memudahkan

pengenalan perubahan-perubahan besar ini, penggunaan kod bar telah dicadangkan.

Sumbangan penting dari tesis ini adalah rekabentuk dua program penslan.

Program pertama adalah program penghasilan kod bar, yang mana ia mampu

menghasilkan lima jenis label-label kod bar yang memainkan peranan utama dalam

kaedah pemusatan setempat yang disarankan. Program kedua adalah program

pengesahan kod bar yang menganalisa fail-fail imej bagi mengesan label kod bar.

Jika label ditemui, program berkenaan akan mengesan dan memaparkan kedua-dua

maklumat yang dikandunginya serta jenis mengkodnya ..

Hasil keputusan berkaitan dengan penghasilan lima jenis label kod bar iaitu

sisipan kod 2 antara 5, kod 3 antara 9, kod barpenutup, kod 128 dan kod 2 antara 5

serta pengesanan dan pengenalan label-label ini dari fail-fail imej akan diperolehi.

Sebagai kesimpulan, tesis ini mencadangkan penyelesaian pada masalah

pemusatan setempat-sendiri bagi robot bergerak yang merupakan pusat paling

penting bagi melaksanakan robot bergerak autonomi, dengan memanfaatkan teknik

pengenalan kod bar berdasarkan-kamera bagi menyokong asas pemusatan setempat

yang diperolehi dari kaedah perhitungan mutlak dalam persekitaran dalaman.

Vll

ACKNOWLEDGEMENTS

First of all, I would like express my utmost thanks and gratitude to Almighty

Allah S. W. T. for giving me the ability to finish this project successfully. Selawat

and salam to his righteous messenger, prophet Muhammad S. A. W.

The author gratefully wish to express his profound appreciation and gratitude

to his supervisor, Dr. Md. Mahmud Hasan, for his supervision, guidance, and

constructive suggestions and comments throughout the duration of the project until it

turns to a real success.

The author also indebted to the members of his supervisory committee, Prof.

Dr. M. N. Faruqui and Dr. Abd Rahman RamIi, for their affectionate guidance,

prompt decision and valuable assistance during this period.

Appreciation also to the assistance rendered by the respective lecturers, staffs,

technicians of the faculty of engineering for providing the facilities required for

undertaking this project.

The author would like to thank his family and his friends for the

encouragement and support without which is impossible for the success of this

project.

viii

I certify that an Examination Committee met on 8 May, 2000 to conduct the final examination of Mahmud M. M. Ben-Hamid on his Master of Science thesis entitled "Mobile Robot Localization Using Bar Codes As Artificial Landmarks" in accordance with U niversiti Pertanian Malaysia (Higher Degree) Act 1980 and Universiti Pertanian Malaysia (Higher Degree) Regulations 1981. The Committee recommends that the candidate be awarded the relevant degree. Members for the Examination Committee are as follows:

Borhanuddin Mohd. Ali, Ph.D. Associate Professor Faculty of Engineering Universiti Putra Malaysia (Chairman)

Md. Mahmud Hassan, Ph.D. Faculty of Engineering Universiti Putra Malaysia. (Member)

M. N. Faruqui, Ph.D. Professor Faculty of Engineering Universiti Putra Malaysia. (Member)

Abd Rahman Ramli, Ph.D. Faculty of Engineering Universiti Putra Malaysia. (Member)

HAZALI MOHA YIDIN, Ph.D. /Deputy Dean of Graduate School

Universiti Putra Malaysia

24 MAY 2000

ix

This thesis was submitted to the Senate of Universiti Putra Malaysia and was accepted as fulfilment of the requirements for the degree of Master of Science.

4w;.Ye.� KAMIS :AWANG, Ph.D. Associate Professor, Dean of Graduate School Universiti Putra Malaysia

Date: 1 3 JUl 2000

x

DECLARATION

I hereby declare that the thesis is based on my original work except for quotations and citations which have been duly acknowledged. I also declare that it has not been previously or concurrently submitted for any other degree at UPM or other institutions.

(Mahmud M. M. Ben-Hamid)

Date: 2. z.. / 5" / 2 dOO

xi

TABLE OF CONTENTS

Page

DEDICATION . . . . . . . .. ... ... .. . ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... .... 11

ABSTRACT . . . ... ... ... ... ... ... .. . ... ... ... ... ... ... ...... ... ... ... ... ... ... ... .... III

ABSTRAK . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v ACKNOWLEDGEMENT ...................................................... ... Vll

APPROVAL SHEETS . . . . , .............................. , ... ... ... ... ... ... ... .... V111

DECLARATION FORM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . x

LIST OF TABLES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . X111

LIST OF FIGURES . . . ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... .... XlV

LIST OF SYMBOLS AND ABBREVIATIONS . . . ... ... ... ... ... ... ... ... ... XVI

CHAPTER

I

IT

III

INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Objective of the Thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Thesis Organization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

LITERATURE REVIEWS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Relative Positioning Measurements . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Odometry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Inertial Navigation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Absolute Positioning Measurements . . . . . . . . . . . . . . . . . . . . . . . . . . .

Magnetic Compasses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Active Beacons . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Global Positioning Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Landmark Navigation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Map-Based Positioning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

BAR CODE TECHNOLOGY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Bar Code Symbology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Code 2 of 5 Specification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Code 2 of 5 Interleaved Specification . . . . . . . . . . . . . . . . . .

Codabar Code Specification . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Code 3 of9 Specification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Code 128 Specification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Reading Bar Code Symbols . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. .

Types of Bar Code Readers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Contact Wands . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Active Non-Contact Readers . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Passive Non-Contact Readers . . . . . . . . . . . . . . . . . . . . . . . . . . .

Bar Code Usage in Mobile Robot Application . . . . . . . . . . . . . . . . . .

Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1 2 3

4 5 5 7 9 9 1 1 13 15

28 32

34 34 38 40 42 44 47 48 5 3 5 4 5 4 57 6 1 63 65

IV

V

VI

METHODOLOGY .. . . . .. .. . . ..... . .. . . . . .. .. . ... . . .. . . .. . . . . ... .

Introduction . . .. . .... ..... . .... . .. . . .. . .... . . .. . . .. ... . . . .. . ....... . Configuration of the Localization System . . .... . ... .......... .

Landmark Recognition Subsystem .... . . .. .. . . .. .. .... . ..... .. .

Artificial Landmarks Generation .. . ... .... ...... .... ...... . ... .

Code 2 of 5 Generation . ... ... .... ..... ..... ..... .. .... .

Code 3 of 9 Generation .. ..... . ... . ..... . ... . . ...... . .. .

Codabar Code Generation . ............ ................ .

Code 128 Generation ... . . . . . . . . . . . . . . . .. . . . . . . . . . ... . .. .

Code 2 of 5 Interleaved Generation .... ..... ....... .. .

Working Principle ....... ..... .. .... .... .. .... . . ... . . . ... . . . . ..... .

Artificial Landmarks Recognition . . . . . . . . ... . . ... . .. . . . .. . . . .. .

Bar Code Label's Detection and Recognition ... ... .

Label's Redundancy ... ............ . ............ ......... .

Readability of the Labels ...................... ........ .

Searching Strategy .... . . . .... ... . . . . ......... . . . . . .... .. .

Encoding Possibilities ... .... .. ............ ... ...... . . . .

The Combination Unit ............... . ............... ..... ....... .

RESULTS AND DISCUSSION .. . .. ... .. . . ........... ........ . Results from the Generation Program . ....... .. .. . .. ... . ...... .

Generating Code 2 of 5 . . . ...... ... .. .. . ............... . .

Generating Code 3 of 9 . . ... . . .. .. . . . . . .. . . . ....... .. . . .

Generating Codabar Code . .. . .. . . ... . .. ..... . . .. ..... . .

Generating Code 128 . . . . . . . . .. . . . . . . . . . . . . . . . . .. . . . . . . . .

Generating Code 2 of 5 Interleaved ......... . . ..... .. .

Results from the Recognition Program ... .. . . ...... . ....... . .. .

Open Function .......... ........ . ................. ...... .

Read Function ... .... .. .. . . . . .. . .. .. ........ . .. . . . . .. .... .

Recognizing a Rotated Label . . . .... . . .. . . . . .. .. ... . . . . . . . .. .... .

Discussion . ... ...... . ... ... . . .. ... .. .......... . .. . . ...... ... ....... .

CONCLUSION . ... .. .. . . ........ . . .. .. .. ......... . . . . .. .. . .. . . ... .

Recommendations .... . . . . ..... ..... . . . . ...... .... .. ...... . . ..... .

xii

6 6 6 6 67 68 69 73 75 75 78 78 8 1 8 1 84 85 86 87 88 89

9 0 90 9 0 9 1 92 93 94 95 9 6 9 6 1 00 102

104 105

REFERENCES . . . ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...... ... ... ... ... 106

VITA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1 1

xiii

LIST OF TABLES

Table Page

3. 1 Code 2 of 5 Character Set . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 41

3.2 Code 2 of 5 Interleaved Character Set . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ... 44

3.3 Codabar Code Character Set . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 46

3.4 Code 3 of 9 Character Set . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48

3.5 Code 128 Character Set . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 1

LIST OF FIGURES

Figure

2. 1 The OmniMate is a Commercially Available

xiv

Page

Fully Omnidirectional Platform . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ... 7

2.2 The Andrew Autogyro Navigator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

2.3 The C- I 00 Fluxgate Compass Engine ... ... ... ... ... ... ... ... ... ... ... .... 10

2.4 The Basic Triangulation Problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

2.5 Dynamic Environment Performance for the Magnavox GPS Engine . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .... 14

2.6 AECL's Natural Landmark Navigation System ......... '" ... ... ... ... .... 17

2.7

2.8

Polarized Retro-reflector Proximity Sensors

Retro-reflector Bar Code Targets Used by the Caterpillar SGV to Triangulate Position

2 0

22

2.9 Komatsu's Z-shaped Landmarks ...... ......... ...... ......... ...... ....... 23

2. 10 The Z-shaped Landmark ...... " ........................................... " 23

2. 1 1 The Geometry of the Z-shaped Landmark . . . . .. . . . . . . . . . . . . . . . . . . .. . . . . . . . 2 4

2. 12 The Odor-Laying! Odor-Sensing Mobile Robot .. ........................ 2 6

2. 13 A Typical Scan of a Room, Produced by the Lidar System . . . . . . . . . . . . 31

2. 14 Calculating Angles for the Angle Histogram .............................. 31

3. 1 Example of EAN- 13 Bar Code Label ...... ...... ......................... 35

3.2a Principle Structure ofa Code 3 of9 Symbol ...... . ... . . . .. ... ....... . . . 37

3.2 b Code 3 of 9 Symbol, Representing the String *BARCODEI * . . . . .. . . 37

3.3 Example of Code 2 of5 ...... '" ............ '" ............ '" ... ... ... ... 41

3.4 Example of Code 2 of5 Interleaved ...... ............................... 43

3.5 Examples of Coda bar Code with Different Start/Stop Characters ...... 45

3.6 Example of Code 3 of9 ...... ......... ...... ...... ......... ......... ...... 47

3.7 Example of Code 128 ............ ............ ...... ............ ...... ...... 49

3.8 The Contact Wand . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 4

3.9 Active Non-Contact Reader ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 58

Figure

3. 10

4. 1

4.2

4.3

4.4

4.5

Passive Non- Contact Reader

Configuration of the Localization System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Bar Code Labels Generation Program . . . . . . ... . . . . . . . . . .. . . . . . . . . . . . . . . .. .

Bar Code Labels Generation Flowchart . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Code 2 of 5 Generation Flowchart

Code 3 of 9 Generation Flowchart

xv

Page

62

67

70

72

74

76

4.6 Codabar Code Generation Flowchart . . . . . . ... ... ... ... ... ... ... ... ... ... 77

4.7 Code 128 Generation Flowchart . . . ... ........ ... ... ... ... ... ... ... ... .... 79

4.8 Code 2 of5 Interleaved Generation Flowchart . . . . . . . . . ... ... ... ....... 80

4.9 Bar Code Labels Recognition Program . . . . .. .. . ............ ...... ...... ... 82

4. 10 Bar Code Labels Recognition Flowchart . . . ...... ... ... ... ... ... ... ... ... 83

4. 1 1 A Rotated Bar Code Label . . . ... ... ... ... ... ... ... ........... ... ... ... ... ... 85

4. 12 An Arbitrary Door Furnished with a Bar Code Label . . . ... ... ... ... ... 86

4. 13 Examples of Quantization Errors . . . . . . . . . ... ......... ............... ...... 87

5. 1

5.2

5.3

5.4

5 .5

5.6

5.7

Code 2 of 5 Generation

Code 3 of 9 Generation

Codabar Code Generation . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . .

Code 128 Generation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Code 2 of 5 Interleaved Generation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Bar Code Recognition Open Function

Bar Code Recognition Read Function

9 1

92

93

94

95

96

97

5.8 Recognition of Code 2 of 5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ... 98

5.9 Recognition of Coda bar Code . . . ... ... ... ... ... ... ... ... ... ... ... ... ..... 98

5. 10 Recognition of Code 128 ... ...... ... ... ... ... ... ... ... ... ... ... ... ... ..... 99

5. 1 1 Recognition of Code 2 of 5 Interleaved . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . 99

5. 12 Recognition of a Rotated Code 3 of 9 Label . . . . . . . . . . . . . . . . . . . . . . . . . . . 101

5. 13 Unrecognized Rotated Code 128 Label . . . . . . .. . ...... .................. 10 1

5. 14 Recognition of a Rotated Code 128 Label . . . ... . . . . . . . . . . . . . . . . . . . . . . . . 102

AGV

UMBmark

TRC

INS

GPS

ARK

ABCL

MDARS

SLNM

CAD

X

LSB

MSB

AIM

Ascn

CCD

mil

LED

IR

UPC

EAN

MFC

PLD

I

N

C

OCR

3

LIST OF SYMBOLS AND ABBREVIATIONS

Automated Guided Vehicles.

University of Michigan Benchmark.

Transition Research Corporation.

Inertial Navigation System.

Global Positioning System.

Autonomous Robot for a Known Environment.

Atomic Energy of Canada Ltd.

Mobile Detection Assessment and Response.

Short-Lived Navigational Marker.

Computer Aided Design.

The Width of the Narrowest Element of the Bar Code.

Least Significant Bit.

Most Significant Bit.

Automatic Identification Manufacturers.

American Standard Code for Information Interchange.

Charge-Coupled Device.

One Thousand of the Inch.

Light Emitting Diode.

Infrared.

Universal Product Code.

European Article Numbering.

Microsoft Foundation Class.

Programmable Logic Device.

Intercharacter Gap.

Wide to Narrow Ratio.

Number of Elements.

Optical Character Recognition.

The Mean Value of the Gray Scale.

xvi

1

CHAPTER I

INTRODUCTION

In order for a mobile robot to perform its assigned tasks, it often requires a

representation of its environment, a knowledge of how to navigate in its

environment, and a method for determining its position in the environment.

The most important result from surveying the literature on mobile robot

positioning is that to date there is no truly elegant solution for the problem. The

many partial solutions can roughly be categorized into two groups: relative and

absolute position measurements. Because of the lack of a single, generally good

method, developers of automated guided vehicles (AGVs) and mobile robots usually

combine two methods, one from each category.

A naive approach to robot localization is to use odometers to measure the

displacements of a robot. This approach, known also as dead-reckoning, is subject to

errors due to external factors beyond robot's control, such as wheel slippage, or

collisions. More importantly, dead reckoning error increases without bound unless

the robot employs sensor feedback in order to recalibrate its position estimate.

A key issue in developing a solution to the localization problem is that of

domain dependence. The majority of localization methods are constructed based on

explicit and/or implicit assumptions about the environment. In the context of

machine vision, for example, many techniques extract domain-dependent features

2

from the image, such as straight lines or comers - features which may not be present

or stable outside of structured office or industrial environments. Hence, a goal of the

method presented here is to achieve domain independence.

Domain independence has been achieved by using bar code labels as artificial

landmarks. The advantage of using the bar code labels are: (1) easy to identify, (2 )

inexpensive, (3) easy to set up, and (4) inconspicuous to human. These landmarks are

useful for the particular domain, while moving to a new domain will not require

much alternation. The method presented here are general in the sense that the same

off-line attaching of the bar code labels in an indoor environment and on-line

position estimation of a mobile robot can be employed in a new domain with little or

no alteration.

Objective of the Thesis

Our aim is to navigate a mobile robot in an indoor environment, especially in

an office building, not in a factory environment. The objective of the thesis is to

present a pragmatic idea which utilizes a camera-based bar code recognition

techniques in order to support mobile robot localization in an indoor environment.

The main idea is to further improve already existing basic localization capabilities,

obtained from dead-reckoning method, by furnishing relevant environmental spots

such as doors, stairs, etc. with semantic information. In order to facilitate the

detection and the recognition of these artificial landmarks the employment of bar

code labels has been proposed.

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Thesis Organization

The thesis proposes a method for mobile robot self-localization using bar

codes as artificial landmarks. The theoretical aspect of the localization problem in

general and our specific solution are considered. Chapter 2 surveys the state-of-the­

art in sensors, systems, methods, and technologies that aim at determining the robot's

position in its environment. The general overview over bar codes, including various

bar code labels and their structure, the different types of bar code readers, and the

usage of bar code labels in mobile robot applications is presented in Chapter 3.

Chapter 4 presents our localization method that uses bar code labels as artificial

landmarks to support basic localization capabilities obtained from dead-reckoning

method, along with the description of two software programs: one for the generating

and the other one for the recognition of five types of bar code labels. Chapter 5

presents the results obtained from the generation and the recognition software

programs with a discussion. Finally, the conclusion is presented in Chapter 6.

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CHAPTER II

LITERATURE REVIEW

Exact knowledge of the position of a vehicle is a fundamental problem in

mobile robot applications. In search for a solution, researchers and engineers have

developed a variety of systems, sensors, and techniques for mobile robot positioning.

This chapter provides a review of relevant mobile robot positioning technologies.

The review defines seven categories for positioning systems: Odometry, Inertial

navigation, Magnetic compasses, Active beacons, Global positioning systems,

Landmark navigation, and Model matching. The characteristics of each category are

discussed and examples of existing technologies are given for each category. The

field of mobile robot navigation is active and vibrant, with more great systems and

ideas being developed continuously.

This section surveys the state-of-the-art in sensors, systems, methods, and

technologies that aim at finding a mobile robot's position in its environment. The

survey are not considering Automated Guided Vehicles (AGVs). Because AGVs use

magnetic tape, buried guide wires, or painted stripes on the ground for guidance.

These vehicles are thus not freely programmable and they can not alter their path in

response to external sensory input (for example, obstacle avoidance).

The most important result from surveying the literature on mobile robot

positioning is that, to date, there is no truly elegant solution for the problem. The

many partial solutions can roughly be categorized into two groups: relative and

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absolute position measurements. The two groups can be further divided into the

following seven categories:

(1) Relative Position Measurements (also called Dead reckoning):

( 1 )Odometry.

(2 )Inertial Navigation.

(IT) Absolute Position Measurements (Reference-based systems):

(3)Magnetic Compasses.

(4)Active Beacons.

(5 )Global Positioning Systems.

(6 )Landmark Navigation.

(7)Model Matching.

Because of the lack of a single good method, developers of mobile robots

usually combine two methods, one from each group.

Relative Positioning Measurements

Odometry

Odometry is the most widely used navigation method for mobile robot

positioning; it provides good short-term accuracy, inexpensive, and allows very high

sampling rates. However, the fundamental idea of odometry is the integration of

incremental motion information over time, which leads inevitably to the unbounded

accumulation of errors. Specifically, orientation errors will cause large lateral

position errors, which increase proportionally with the distance traveled by the robot.

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Despite these limitations, most researchers agree that odometry is an important part

of a robot navigation system and that navigation tasks will be simplified if odometric

accuracy can be improved. For example Cox (1991), Byrne et al. (1992 ), and

Chenavier and Crowley (1992 ), propose methods for fusing odometric data with

absolute position measurements to obtain more reliable position estimation.

Odometry is based on simple equations (Borenstein et a1., 199 6 ), which hold true

when wheel revolutions can be translated accurately into linear displacement relative

to the floor.

However, in case of wheel slippage and some other more subtle causes, wheel

rotations may not translate proportionally into linear motion. The resulting errors can

be categorized into one of two groups: systematic errors and non-systematic errors

(Borenstein and Feng, 1996 ). Systematic errors are those resulting from kinematic

imperfections of the robot, e.g., unequal wheel diameters or uncertainty about the

exact wheel-base. Non-systematic errors are those that result from the interaction of

the floor with the wheels, e.g., wheel slippage or bumps and cracks. Typically, when

a mobile robot system is installed with a hybrid odometryllandmark navigation

system, the density in which the landmarks must be placed in the environment is

determined empirically and is based on the worst-case systematic errors.

Borenstein (1995 ), developed a method for detecting and rejecting non­

systematic odometry errors in mobile robots. With this method, two collaborating

platforms continuously and mutually correct their non-systematic (and certain

systematic) odometry errors even while both platforms are in motion. A commercial

version of this robot, shown in Figure 2.1, is available from TRC under the name

"OmniMate. "

Figure 2.1: The OmniMate is a Commercially Available Fully Omnidirectional Platform.

(http://www-personal.engin.umich.edul-johannb/OrrmiMate.htm)

Inertial Navigation

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Inertial navigation uses gyroscopes and accelerometers to measure rate of

rotation and acceleration, respectively. Measurements are integrated once (or twice,

for accelerometers) to yield position. Inertial navigation systems have the advantage

that they are self-contained, that is, they don't need external references. However,

inertial sensor data drift with time because of the need to integrate rate data to yield

position; any small constant error increases without bound after integration. Inertial

sensors are thus mostly unsuitable for accurate positioning over an extended period

of time.

Accelerometers

Test results from the use of accelerometers for mobile robot navigation have

been generally poor (Borenstein et aI., 1996). Accelerometers also suffer from

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extensive drift, and they are sensitive to uneven ground because any disturbance

from a perfectly horizontal position will cause the sensor to detect a component of

the gravitational acceleration. One low cost inertial navigation system aimed at

overcoming the latter problem included a tilt sensor (Barshan and Durrant-Whyte,

1993; 1995). The tilt information provided by the tilt sensor was supplied to the

accelerometer to cancel the gravity component projecting on each axis of the

accelerometer.

Gyroscopes

Gyroscopes, also known as rate gyros or just gyros, are of particular

importance to mobile robot positioning because they can help compensate for the

foremost weakness of odometry: in an odometry-based positioning method, any

small momentary orientation error will cause a constantly growing lateral position

error. For this reason it would be of great benefit if orientation errors could be

detected and corrected immediately. Until recently, highly accurate gyros were too

expensive for mobile robot applications. For example, a high-quality Inertial

Navigation System (INS) such as those found in a commercial airliner would have a

typical drift of about 1850 meters (1 nautical mile) per hour of operation, and cost

between $50K to $70K (Byrne et aI., 1992).

However, very recently fiber-optic gyros, also called laser gyros, which are

known to be very accurate, have fallen dramatically in price and have become a very

attractive solution for mobile robot navigation. One commercially available laser

gyro is the Autogyro Navigator from Andrew Corp. ANDREW, shown in Figure 2.2.